E-Mail Security Using Spam Mail Detection and Filtering System

International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 5 Issue: 10 01 – 03
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IJRITCC | October 2017, Available @ http://www.ijritcc.org
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E-Mail Security Using Spam Mail Detection and Filtering System
1
K. Ravikumar, 2
P. Gandhimathi
1
Asst.professor, Dept.of.Computer Science, Tamil University, Thanjavur-613010
2
Research Scholar, Dept.of.Computer Science, Tamil University, Thanjavur-613010
Abstract: Electronic mail, also known as email or e-mail, is a method of exchanging digital messages from an author to one or more recipients.
Email is the most efficient way to communicate or transfer our data from one to another. While transferring or communicating through email
there is the possibility of misbehave. In the existing system Spam method is used to avoid the unwanted Email receiving. Email spam, also
known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted email messages,
frequently with commercial content, in large quantities to an indiscriminate set of recipients. But in Spam method there is no way to prevent the
unwanted messages or Email receiving. To solve these unwanted messages or Email receiving we propose the concept Email misbehave
blocking system. In the proposed method we permanently prevent the incoming unwanted messages or Email through blocking system.
Keywords: Email, Digital, UCE, Spam
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I. INTRODUCTION
An e-mail is considered “spam” when a massive
number of them are sent to multiple recipients. Spam email
is usually used for advertisement or marketing. These
unwanted emails cause drawbacks to the recipient, and
consume the users’ network resources. The disadvantages of
spam emails have been addressed in many occasions. In
some cases for a single user 9 out of 10 emails are spams
that fill his/her inbox. The United States Federal Trade
Commission described that 66% of spams have false
information somewhere in the message and 18% of spams
advertise “Adult” material. According to another report 12%
of users spend half hour or more per day dealing with spam
emails. There are several major problems with spam mails.
First of all, they are high in volume and fill in mailbox of
users. Secondly, there is no correlation between receivers’
area of interests and the contents of spam mails. Thirdly,
they cost money for ISPs because the bandwidth and the
memory of system are wasted. Finally, Spam e-mails cause
a lot of security problems because most of them include
Trojan, Malwares, and viruses. Many filtering techniques
have been developed to control the flow of spam emails.
Unfortunately, even with these available techniques, the
number of spam emails is growing and the flow has not been
controlled completely. The setback is that there is no actual
solution because a spammer; an unidentified user with
enough knowledge is able to be familiar with the logic of the
filtering mechanisms. As a result, bypassing the filter and
sending the spam seems not to be a difficult task for such
spammers. In such cases, the spam emails are not detected
and are considered as legitimate ones.
There are studies regarding spam email filtering.
The common issue with the usage of all of these techniques
is that the filtering systems are set up in the receiver mail
server, consequently, causing network load and wasting
network resources. To preserve network resources such as
bandwidth and memory, and to reduce network load, this
paper proposes to locate spam email filtering in the sender
mail server rather than the receiver mail server.
Moreover, this paper by experimental results shows
that this novel approach works more efficiently compared
with the previously proposed approaches. This paper is
organized as follows. In section 2, the related work to the
subject will be highlighted. The Overview of email system
and its operation are described in. Our proposal and the
experiment results are presented in respectively.
As stated before, there are many filtering
techniques to stop the flow of spam emails to mail boxes.
Figure 1 simply illustrates the classification of spam email
filtering techniques. The classification includes list-based
filtering, static algorithm, and IP-based filtering. The list-
based filtering is classified into three categories; Blacklist,
Whitelist, and Greylist. Static algorithm is classified into
content-based, and the rulebased filtering. Finally, IP-based
filtering consists of revers-lookup. In the Blacklist filtering,
the IP address and the domain name of the sender server is
stored in a list called Blacklist and the emails from that IP
address and domain are blocked. Then, based on the policy
of the receiver side, the emails from the Blacklisted IP
addresses are deleted or sent to spam folder. Conversely,
there are some limitations for the Blacklist filtering. First,
since the spammer uses several IP addresses with a variety
of domain names, updating these lists is a difficult task for
the client. Consequently, updating the Blacklist regularly is
costly.
Second, Blacklist filtering may result in
identification of an email as false negative because of
minimal control in this methodology. On the other side of
the Blacklist, is the Whitelist filtering. In this technique, any
user stores his/her email contacts in a list called the

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Whitelist. Therefore, any received email with the
correspondent address from this list is accepted, and all
other addresses out of this list are considered uncertain. In
this technique, also there are certain obstacles. The obvious
one is that, since the sender is unidentified and
unpredictable, it is difficult to insert all possible sender
addresses in this list. Similar to the Blacklist, the Whitelist
filtering needs to be updated regularly; which is a costly task
for the user. Another major issue is that if the email address
of a spammer is added in the Whitelist of an email client
once, this will provide access to all of the addresses in the
Whitelist of that specific client without any boundaries or
limits. As a result, this will ensure the spammer more
reachable email addresses.
II. PROBLEM DEFINITION
An e-mail is considered “spam” when a massive number of
them are sent to multiple recipients. Spam email is usually
used for advertisement or marketing. These unwanted emails
cause drawbacks to the recipient, and consume the users’
network resources. The disadvantages of spam emails have
been addressed in many occasions. In some cases for a
single user 9 out of 10 emails are spams that fill his/her
inbox. The United States Federal Trade Commission
described that 66% of spams have false information
somewhere in the message and 18% of spams advertise
“Adult” material. According to another report 12% of users
spend half hour or more per day dealing with spam emails.
There are several major problems with spam mails. First of
all, they are high in volume and fill in mailbox of users.
Secondly, there is no correlation between receivers’ area of
interests and the contents of spam mails. Thirdly, they cost
money for ISPs because the bandwidth and the memory of
system are wasted. Finally, Spam e-mails cause a lot of
security problems because most of them include Trojan,
Malwares, and viruses. Many filtering techniques have been
developed to control the flow of spam emails.
In recent years Email spam is sent via
"zombie networks", from personal computers in homes and
offices around the globe. Detecting spam based on the
content of the email, either by detecting keywords or by
statistical means i.e., content or non-content based, is widely
used technique to find spam messages. Content based
statistical means or detecting keywords can be very accurate
when they are correctly tuned to the types of legitimate
email that an individual gets. The content also doesn't
determine whether the email was either unsolicited or bulk,
the two key features of spam. Non-content base statistical
means can help lower false positives because it looks at
statistical means vs. blocking based on content/keywords.
 We can only move the incoming spam messages to
the spam folder but we can’t prevent receiving
spam mails.
 It occupies the more mail memory wastage by
receiving this kind of spammed mails.
 We need to delete the unwanted spam messages
manually; it is time consuming process to the users
and it takes the request or response service from the
email server.
 By this method there is the possibility of receiving
virus or warm emails from the spammer.
We propose an Email spam blocking system, which
blocks the incoming spam or unwanted messages. In this
method users can able to prevent the spam messages
entering into their inbox or spam box. Since the unwanted
emails are blocked from the spammer. So we can save the
mail memories, because of mail memories are limited we
need to save our memory. We are not in need to view our
Spam box. In case of emergency communication the
blocked person email can be unblocked by the recipient user
who blocked the spammer. This emergency communication
is possible only once for a blocked account. If they are
unblocked that particular account they can communicate
frequently like normal user, if they are misusing the
emergency communication then the same spammer account
can be blocked again by then the spammer cannot
communicate with the recipient in future.
ADVANTAGES:
 By this method we can permanently block the
receiving messages.
 There is no mail memory wastage for the unwanted
emails, because the unwanted emails are blocked.
 There is no need of opening the spam folder so we
can save our time.
 By this method there is no way of receiving virus
or warm emails from the spammer.
III. CONCLUSION
A content based classification of spam mails with
fuzzy word ranking. There are many classifiers and filters
available for classifying and filtering spam mails. This study
analyzed the previous related works. The proposed work
used two sets of linguistic terms for ranking and classifying
spam mails. This method has extracted only the features
from the content of an email instead of extracting all the
features from the mail. The actual words are extracted from
the inbox of an email are compared with a list of spam
words in the database and the words are categorized
according to its rank value. This input value is passed to the
fuzzy inference system. FIS classifies the spam and

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produces the output. This work obtains a better result from
ranking and classifying of spam words. An efficient
approach for spam email detection. To shift the location of
spam email filtering system from receiver mail server to
sender mail server. The purpose of this novel idea is to
detect spam emails in the shortest time and consequently to
prevent wasting the network resources from misusage of
spammers. In addition, by experimental results we proved
that our idea is efficient because just the resources in the
sender side are accessed. This implies that if an email is
identified as spam one, the receiver’s bandwidth and
memory is preserved which will assure a better
performance. Finally, by locating the filtering system in the
sender mail server; the processed time becomes n times less
than the time when the filtering system is in the receiver
mail server when n indicates the number of processed
emails.
REFERENCES
[1] C. MacFarlane, (2003), “FTC Measures False Claims
Inherent in Random Spam,” Federal Trade Commission,
http://www.ftc.gov/opa/2003/04/spamrpt.shtm, Accessed
Jul. 20, 2011.
[2] L. Nosrati & A. Nemaney Pour, “Dynamic Concept Drift
Detection for Spam Email Filtering,” Proceedings of
ACEEE 2nd International Conference on Advances
Information and Communication Technologies (ICT 2011),
Amsterdam, Netherlands, pp. 124-126, Dec. 2011.
[3] A. Ramachandran, D. Dagon & N. Feamster, “Can DNS-
Based Blacklists Keep Up with Bots?,” The Third
Conference on Email and Anti-Spam (CEAS 2006),
California, USA, pp.1-2, Jul. 2006.
[4] J. Goodman, “Spam: Technologies and Policies,” White
Paper, Microsoft research, pp.1-19, Feb. 2004.
International Journal of Network Security & Its
Applications (IJNSA), Vol.4, No.2, March 2012 62
[5] A.Ramachandran & N. Feamster, “Understanding the
Network-Level Behavior of Spammers,” Proceedings of the
2006 conference on Applications, technologies,
architectures, and protocols for computer communications
(SIGCOMM 2006), Pisa, Italy, pp. 291-302, Sep. 2006.
[6] E. Harris, (2003), “Greylisting: The Next Step in the Spam
Control War,” White Paper,
http://projects.puremagic.com/greylisting/whitepaper.html,
Accessed Dec. 20, 2011.
[7] J.R. Levine, “Experience with Greylisting,” Proceedings of
Second Conference on Email and Anti-Spam (CEAS 2005),
CA, USA, pp. 1-2, Jul. 2005.
[8] P. Graham, “Better Bayesian filtering,” MIT Spam
Conference, Jun. 2003..
[9] H. Yin & Z. Chaoyang, “An Improved Bayesian Algorithm
for Filtering Spam E-Mail,” IEEE 2nd International
Symposium on Intelligence Information Processing and
Trusted Computing(IPTC 2011), Huangzhou, China, pp.
87-90, Oct. 2011.
[10] A. Ciltik & T. Gungor, (2008), “Time-efficient spam e-mail
filtering using n-gram models,” Elsevier,Pattern
Recognition Letters, Vol. 29, No. 1, pp. 19-33.

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